Article: Personalised service and sentiment analysis for intelligent customer relationship management systems based on natural language processing Journal: International Journal of Ad Hoc and Ubiquitous Computing (IJAHUC) 2026 Vol.52 No.5 pp.76 - 86 Abstract: To enhance acoustic affect recognition, this study proposes the ECAPA-BGTDNN framework, which combines SE-Res2Block and Bi-GRU. It first extracts speech features via MFCC, enhances feature expressiveness through SE-Res2Block, captures temporal attributes using Bi-GRU, and achieves high-precision emotion classification via pooling and classification algorithms. Moreover, the recognised emotional states are further transformed into adaptive service strategies, enabling intelligent and real-time adjustment of customer interaction. Experimental results demonstrate that ECAPA-BGTDNN achieves 0.706 precision, 0.729 recall, and 0.717 F1-score, outperforming ECAPA-TDNN by 2.0%, 3.2%, and 2.3%, respectively. In practical deployment on a self-constructed customer voice set, the model further attains 0.837 average precision, enabling stable affect tracking during full conversations exceeding 50 seconds. Inderscience Publishers - linking academia, business and industry through research

Title: Personalised service and sentiment analysis for intelligent customer relationship management systems based on natural language processing

Authors: Beibei Li

Addresses: School of Economics and Management, Shanghai Maritime University, Shanghai, 201306, China; Business School, Shanghai Jian Qiao University, Shanghai, 201302, China

Abstract: To enhance acoustic affect recognition, this study proposes the ECAPA-BGTDNN framework, which combines SE-Res2Block and Bi-GRU. It first extracts speech features via MFCC, enhances feature expressiveness through SE-Res2Block, captures temporal attributes using Bi-GRU, and achieves high-precision emotion classification via pooling and classification algorithms. Moreover, the recognised emotional states are further transformed into adaptive service strategies, enabling intelligent and real-time adjustment of customer interaction. Experimental results demonstrate that ECAPA-BGTDNN achieves 0.706 precision, 0.729 recall, and 0.717 F1-score, outperforming ECAPA-TDNN by 2.0%, 3.2%, and 2.3%, respectively. In practical deployment on a self-constructed customer voice set, the model further attains 0.837 average precision, enabling stable affect tracking during full conversations exceeding 50 seconds.

Keywords: NLP; customer management; personalised services; affective computing.

DOI: 10.1504/IJAHUC.2026.154122

International Journal of Ad Hoc and Ubiquitous Computing, 2026 Vol.52 No.5, pp.76 - 86

Received: 28 Oct 2025
Accepted: 22 Dec 2025
Published online: 12 Jun 2026 *

Title: Personalised service and sentiment analysis for intelligent customer relationship management systems based on natural language processing

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